Novel power-transformed statistical distributions in wind speed modeling

Abstract

Wind energy is one of the fastest-growing renewable power sources, and its accurate assessment requires statistical distributions capable of capturing the inherent variability in wind speed. This study introduces six power-transformed distributions, Generalized Power Generalized Weibull (GPGWD), Power Generalized Weibull (PGWD), Power Ishita (PID), Power Lindley (PLD), Power Akash (PAD), and Power Shanker (PSD), as new alternatives for wind energy modeling. Using wind speed data from Eskişehir, Türkiye, the proposed models were compared with common benchmark distributions. Results show that GPGWD and PGWD consistently outperform benchmarks, with GPGWD providing the highest accuracy, particularly in R², RMSE, and most KS and CHI tests; PGWD ranks second. Although PSD, PLD, and some other models perform well at stations with irregular or low wind regimes in metrics such as MAPE and PDE, overall findings indicate that GPGWD is the most flexible model across most stations.